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Learning an accurate entity resolution model from crowdsourced labels
2014
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication - ICUIMC '14
We investigated the use of supervised learning methods that use labels from crowd workers to resolve entities. Although obtaining labeled data by crowdsourcing can reduce time and cost, it also brings challenges (e.g., coping with the variable quality of crowdgenerated data). First, we evaluated the quality of crowd-generated labels for actual entity resolution data sets. Then, we evaluated the prediction accuracy of two machine learning methods that use labels from crowd workers: a
doi:10.1145/2557977.2558060
dblp:conf/icuimc/WangOKK14
fatcat:2hwx52fnfrcqlbge7dldtx767e